ValeriaLaamraniBeaudoin2012

Référence

Valeria, O., Laamrani, A., Beaudoin, A. (2012) Monitoring the state of a large boreal forest region in eastern Canada through the use of multitemporal classified satellite imagery. Canadian Journal of Remote Sensing, 38(1):91-108. (URL )

Résumé

Multitemporal classification of Landsat imagery was used to measure and monitor the state of the forest over a large area (11.6 million ha) of boreal forest in eastern Canada using four criteria for a 20 year period (1985–2005). The Enhancement-Classification Method was used in this study. Forty-eight thematic classes based on Canada's National Forest Inventory were identified, then grouped into 13 indicators, and reorganized within four main criteria: (i) forest versus nonforest land cover, (ii) forest development stage, (iii) forest cover type, and (iv) forest cover density. Validation based on 2973 high-resolution geo-referenced digital aerial colour photos of the 2005 classified images showed an overall accuracy of the four criteria of 83%, 68%, 58%, and 62%, respectively. The change in each indicator between 1985 and 2005 could be summarized as: (i) a decrease in productive forest area of 0.4% (approx. 43 000 ha); (ii) a 4.6% decrease in mature stand area, with a concomitant increase in areas classified as vegetated (1.3%) and regenerated (3.4%); (iii) concentration of harvesting pressure on coniferous and mixed stands with respective reductions of 8.2% and 0.8%, due to their conversion to deciduous stands; and (iv) an increase in low-density stands (3.1%) and a decrease in high-density stands (8.3%). These results demonstrate that medium-resolution (30 m) remote sensing tools can be used both to monitor the state of the boreal forest and to produce key indicators, which were extracted from the multidate Landsat satellite imagery.

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@ARTICLE { ValeriaLaamraniBeaudoin2012,
    AUTHOR = { Valeria, O. and Laamrani, A. and Beaudoin, A. },
    TITLE = { Monitoring the state of a large boreal forest region in eastern Canada through the use of multitemporal classified satellite imagery },
    JOURNAL = { Canadian Journal of Remote Sensing },
    YEAR = { 2012 },
    VOLUME = { 38 },
    PAGES = { 91-108 },
    NUMBER = { 1 },
    ABSTRACT = { Multitemporal classification of Landsat imagery was used to measure and monitor the state of the forest over a large area (11.6 million ha) of boreal forest in eastern Canada using four criteria for a 20 year period (1985–2005). The Enhancement-Classification Method was used in this study. Forty-eight thematic classes based on Canada's National Forest Inventory were identified, then grouped into 13 indicators, and reorganized within four main criteria: (i) forest versus nonforest land cover, (ii) forest development stage, (iii) forest cover type, and (iv) forest cover density. Validation based on 2973 high-resolution geo-referenced digital aerial colour photos of the 2005 classified images showed an overall accuracy of the four criteria of 83%, 68%, 58%, and 62%, respectively. The change in each indicator between 1985 and 2005 could be summarized as: (i) a decrease in productive forest area of 0.4% (approx. 43 000 ha); (ii) a 4.6% decrease in mature stand area, with a concomitant increase in areas classified as vegetated (1.3%) and regenerated (3.4%); (iii) concentration of harvesting pressure on coniferous and mixed stands with respective reductions of 8.2% and 0.8%, due to their conversion to deciduous stands; and (iv) an increase in low-density stands (3.1%) and a decrease in high-density stands (8.3%). These results demonstrate that medium-resolution (30 m) remote sensing tools can be used both to monitor the state of the boreal forest and to produce key indicators, which were extracted from the multidate Landsat satellite imagery. },
    DOI = { 10.5589/m12-014 },
    EPRINT = { http://pubs.casi.ca/doi/pdf/10.5589/m12-014 },
    URL = { http://pubs.casi.ca/doi/abs/10.5589/m12-014 },
}

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